Multiparametric magnetic resonance imaging for the differential diagnosis between granulomatous prostatitis and prostate cancer: a literature review to an intriguing diagnostic challenge

Multiparametric magnetic resonance imaging (mpMRI) is currently the most effective diagnostic tool for detecting prostate cancer (PCa) and evaluating adenocarcinoma-mimicking lesions of the prostate gland, among which granulomatous prostatitis (GP) represents the most interesting diagnostic challenge. GP consists of a heterogeneous group of chronic inflammatory lesions that can be differentiated into four types: idiopathic, infective, iatrogenic, and associated with systemic granulomatous disease. The incidence of GP is growing due to the increase in endourological surgical interventions and the adoption of intravesical instillation of Bacillus Calmette-Guerin in patients with non-muscle invasive bladder cancer; therefore, the difficulty lies in identifying specific features of GP on mpMRI to avoid the use of transrectal prostate biopsy as much as possible

Compact Tactile Sensors for Robot Fingers

Compact transducer arrays that measure spatial distributions of force or pressure have been demonstrated as prototypes of tactile sensors to be mounted on fingers and palms of dexterous robot hands. The pressure- or force-distribution feedback provided by these sensors is essential for the further development and implementation of robot-control capabilities for humanlike grasping and manipulation

Learning from sustainable development: education in the light of public issues

Education for sustainable development (ESD) is increasingly affecting environmental education policy and practice. In this article we show how sustainable development is mainly seen as a problem that can be tackled by applying the proper learning processes and how this perspective translates sustainability issues into learning problems of individuals. We present a different perspective on education in the context of sustainable development based on novel ways of thinking about citizenship education and emphasizing the importance of presenting issues of sustainable development as ‘public issues’, as matters of public concern. From this point of view, the focus is no longer on the competences that citizens must achieve, but on the democratic nature of the spaces and practices in which participation and citizenship can develop

Transverse prostate maximum sectional area can predict clinically significant prostate cancer in PI-RADS 3 lesions at multiparametric magnetic resonance imaging

BackgroundTo evaluate multiparametric magnetic resonance imaging (mpMRI) parameters, such as TransPA (transverse prostate maximum sectional area), TransCGA (transverse central gland sectional area), TransPZA (transverse peripheral zone sectional area), and TransPAI (TransPZA/TransCGA ratio) in predicting prostate cancer (PCa) in prostate imaging reporting and data system (PI-RADS) 3 lesions. MethodsSensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), the area under the receiver operating characteristic curve (AUC), and the best cut-off, were calculated. Univariate and multivariate analyses were carried out to evaluate the capability to predict PCa. ResultsOut of 120 PI-RADS 3 lesions, 54 (45.0%) were PCa with 34 (28.3%) csPCas. Median TransPA, TransCGA, TransPZA and TransPAI were 15.4cm(2), 9.1cm(2), 5.5cm(2) and 0.57, respectively. At multivariate analysis, location in the transition zone (OR=7.92, 95% CI: 2.70-23.29, P<0.001) and TransPA (OR=0.83, 95% CI: 0.76-0.92, P<0.001) were independent predictors of PCa. The TransPA (OR=0.90, 95% CI: 0.082-0.99, P=0.022) was an independent predictor of csPCa. The best cut-off of TransPA for csPCa was 18 (Sensitivity 88.2%, Specificity 37.2%, PPV 35.7%, NPV 88.9%). The discrimination (AUC) of the multivariate model was 0.627 (95% CI: 0.519-0.734, P<0.031). ConclusionsIn PI-RADS 3 lesions, the TransPA could be useful in selecting patients requiring biopsy

A novel patient-derived tumorgraft model with TRAF1-ALK anaplastic large-cell lymphoma translocation.

Although anaplastic large-cell lymphomas (ALCL) carrying anaplastic lymphoma kinase (ALK) have a relatively good prognosis, aggressive forms exist. We have identified a novel translocation, causing the fusion of the TRAF1 and ALK genes, in one patient who presented with a leukemic ALK+ ALCL (ALCL-11). To uncover the mechanisms leading to high-grade ALCL, we developed a human patient-derived tumorgraft (hPDT) line. Molecular characterization of primary and PDT cells demonstrated the activation of ALK and nuclear factor kB (NFkB) pathways. Genomic studies of ALCL-11 showed the TP53 loss and the in vivo subclonal expansion of lymphoma cells, lacking PRDM1/Blimp1 and carrying c-MYC gene amplification. The treatment with proteasome inhibitors of TRAF1-ALK cells led to the downregulation of p50/p52 and lymphoma growth inhibition. Moreover, a NFkB gene set classifier stratified ALCL in distinct subsets with different clinical outcome. Although a selective ALK inhibitor (CEP28122) resulted in a significant clinical response of hPDT mice, nevertheless the disease could not be eradicated. These data indicate that the activation of NFkB signaling contributes to the neoplastic phenotype of TRAF1-ALK ALCL. ALCL hPDTs are invaluable tools to validate the role of druggable molecules, predict therapeutic responses and implement patient specific therapies

Quinine, an old anti-malarial drug in a modern world: role in the treatment of malaria

Quinine remains an important anti-malarial drug almost 400 years after its effectiveness was first documented. However, its continued use is challenged by its poor tolerability, poor compliance with complex dosing regimens, and the availability of more efficacious anti-malarial drugs. This article reviews the historical role of quinine, considers its current usage and provides insight into its appropriate future use in the treatment of malaria. In light of recent research findings intravenous artesunate should be the first-line drug for severe malaria, with quinine as an alternative. The role of rectal quinine as pre-referral treatment for severe malaria has not been fully explored, but it remains a promising intervention. In pregnancy, quinine continues to play a critical role in the management of malaria, especially in the first trimester, and it will remain a mainstay of treatment until safer alternatives become available. For uncomplicated malaria, artemisinin-based combination therapy (ACT) offers a better option than quinine though the difficulty of maintaining a steady supply of ACT in resource-limited settings renders the rapid withdrawal of quinine for uncomplicated malaria cases risky. The best approach would be to identify solutions to ACT stock-outs, maintain quinine in case of ACT stock-outs, and evaluate strategies for improving quinine treatment outcomes by combining it with antibiotics. In HIV and TB infected populations, concerns about potential interactions between quinine and antiretroviral and anti-tuberculosis drugs exist, and these will need further research and pharmacovigilance

Digital Pheromone Implementation of PSO with Velocity Vector Accelerated by Commodity Graphics Hardware

In this paper, a model for Graphics Processing Unit (GPU) implementation of Particle Swarm Optimization (PSO) using digital pheromones to coordinate swarms within ndimensional design spaces is presented. Particularly, the velocity vector computations are carried out on graphics hardware. Previous work by the authors demonstrated the capability of digital pheromones within PSO for searching n-dimensional design spaces with improved accuracy, efficiency and reliability in serial, parallel and GPU computing environments. The GPU implementation was limited to computing the objective function values alone. Modern GPUs have proven to outperform the number of floating point operations when compared to CPUs through inherent data parallel architecture and higher bandwidth capabilities. This paper presents a method to implement velocity vector computations on a GPU along with objective function evaluations. Three different modes of implementation are studied and presented - First, CPU-CPU where objective function and velocity vector are calculated on CPU alone. Second, GPU-CPU where objective function is computed on the GPU and velocity vector is computed on GPU. Third, GPU-GPU where objective function and velocity vector are both evaluated on the GPU. The results from these three implementations are presented followed by conclusions and recommendations on the best approach for utilizing the full potential of GPUs for PSO